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Constructing an Artificial Intelligence-Driven Multilingual Medical Health Education Chatbot with Domain-Specific

Tai-Liang Chen¹, Yi-Hui Liu²

¹Department of Digital Content Application and Management, Wenzao Ursuline University of Languages, Kaohsiung, Taiwan.

|July 3, 2026

Summary

This summary is machine-generated.

This study developed an AI chatbot to break down language barriers in healthcare, improving communication for diverse populations. The multilingual system demonstrated user acceptance and cross-cultural applicability, enhancing patient understanding.

Keywords:

conversational artificial intelligence health care communication health education multilingual chatbot system usability

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Area of Science:

Health Informatics
Artificial Intelligence in Healthcare
Cross-cultural Communication

Background:

Language barriers significantly impede effective healthcare delivery for migrant and multicultural populations.
Existing communication tools often lack the capacity for nuanced, multilingual health education.

Purpose of the Study:

To design, implement, and evaluate an AI-driven multilingual medical chatbot.
To enhance health care communication and accessibility for diverse patient groups.

Main Methods:

Developed a user-state-driven multi-layer logical architecture for efficient multilingual routing.
Implemented a hybrid retrieval-generative framework combining expert content with ChatGPT.
Deployed the chatbot on the LINE platform and conducted usability testing with 85 participants.

Main Results:

The AI chatbot demonstrated satisfactory usability and user acceptance across diverse linguistic and cultural backgrounds.
Consistent performance was observed across different nationality groups, indicating cross-cultural applicability.
The system proved effective in reducing language barriers and improving patient understanding.

Conclusions:

The proposed AI chatbot architecture offers a scalable, efficient, and practical solution for multilingual healthcare communication.
This technology can significantly improve patient understanding and access to health information in real-world clinical settings.
The findings support the potential of AI to address critical communication challenges in diverse healthcare environments.